Slicing Resource Allocation Based on Dueling DQN for eMBB and URLLC Hybrid Services in Heterogeneous Integrated Networks

Chen, Geng; Shao, Rui; Shen, Fei; Zeng, Qingtian

doi:10.3390/s23052518

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…The table reflects the main approaches for resource sharing and the methods used to implement them, as well as how prioritized URLLC traffic affects eMBB traffic. Among these approaches, network slicing [24,25] is considered, along with other methods such as dynamic multiplexing [26,27] and reconfigurable intelligent surface (RIS)-aided multiplexing [15]. Due to the stringent delay requirements of URLLC traffic, it is typically prioritized, which can have various impacts on eMBB transmission.…”

Section: Related Workmentioning

confidence: 99%

Retrial Queueing System for Analyzing Impact of Priority Ultra-Reliable Low-Latency Communication Transmission on Enhanced Mobile Broadband Quality of Service Degradation in 5G Networks

Makeeva,

Kochetkova,

Alkanhel

2023

Mathematics

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Fifth generation (5G) networks support ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB). The coexistence of URLLC and eMBB is often organized by non-orthogonal multiple access (NOMA), giving priority to URLLC and resulting in eMBB quality of service (QoS) degradation. In this paper, we address the issue of joint URLLC and eMBB transmission, focusing on the problem from the perspective of delay-tolerant eMBB. Due to the priority given to URLLC, we assume that an eMBB session may be interrupted if there are no free resources available for URLLC or delayed when a new eMBB session arrives. To make the scheme more flexible, we propose that interrupted and delayed eMBB sessions periodically check for free resources, rather than continuously. To analyze this scenario, we propose a retrial queuing system with two retrial buffers (orbits) for interrupted and delayed eMBB sessions. The stationary probability distribution, provided in matrix form by recursive formulas, is presented. The paper concludes with a numerical example showing that the scheme with two buffers, compared to one buffer, practically doubles the average number of active eMBB sessions while keeping the interruption probability below 0.001. We provide an illustration of the configuration of eMBB retrial rates to meet its QoS requirements.

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Section: Related Workmentioning

confidence: 99%

Retrial Queueing System for Analyzing Impact of Priority Ultra-Reliable Low-Latency Communication Transmission on Enhanced Mobile Broadband Quality of Service Degradation in 5G Networks

Makeeva,

Kochetkova,

Alkanhel

2023

Mathematics

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“…It adopts the experience replay mechanism, which improves the utilization of the training experience and attenuates the effect brought by the correlation of data sequences. It adopts a single-step updating method with high exploration efficiency [57,58]. Hence, it can be a valuable technique for optimizing the comprehensive utilization of satellite resources.…”

Section: Resource Arrangement In the Timeline By Idqnmentioning

confidence: 99%

A Hierarchical Resource Scheduling Method for Satellite Control System Based on Deep Reinforcement Learning

Li,

Guo,

Meng

et al. 2023

Electronics

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Space-based systems providing remote sensing, communication, and navigation services are essential to the economy and national defense. Users’ demand for satellites has increased sharply in recent years, but resources such as storage, energy, and computation are limited. Therefore, an efficient resource scheduling strategy is urgently needed to satisfy users’ demands maximally and get high task execution benefits. A hierarchical scheduling method is proposed in this work, which combines improved ant colony optimization and an improved deep Q network. The proposed method considers the quality of current task execution and resource load balance. The entire resource scheduling process contains two steps, task allocation and resource scheduling in the timeline. The former mainly implements load balance by improved ant colony optimization, while the latter mainly accomplishes the high task completion rate by an improved deep Q network. Compared with several other heuristic algorithms, the proposed approach is proven to have advantages in terms of CPU runtime, task completion rate, and resource variance between satellites. In the simulation scenarios, the proposed method can achieve up to 97.3% task completion rate, with almost 50% of the CPU runtime required by HAW and HADRT. Furthermore, this method has successfully implemented load balance.

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“…However, the aforementioned methods suffer from inefficient search strategies, often resulting in network structures that do not perform well in practical engineering scenarios [26][27][28]. To address these limitations in applying CNN to fault diagnosis, two key challenges need to be overcome:…”

Section: Introductionmentioning

confidence: 99%

A reinforcement neural architecture search convolutional neural network for rolling bearing fault diagnosis

Li,

Jiang,

Wang

et al. 2023

Meas. Sci. Technol.

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The complexity of machinery makes accurate identification of rolling bearing fault signals difficult. Convolutional neural networks (CNN) have made some progress, but they rely on the expertise of the network designer and the iterative process of optimizing numerous parameters. Therefore, there is an urgent need to develop a method that reduces the threshold for designing CNNs for a given task. In this article, we propose a reinforcement neural architecture search CNN to address this problem. Firstly, we design a neural architecture search algorithm that can generate different types of sub-networks specifically for fault diagnosis tasks. Secondly, we execute a reinforcement learning-based search strategy to discover promising sub-networks. Furthermore, we enhance the performance of the sub-network by improving the optimizer and training parameters. We conduct extensive experiments using two different types of datasets and verify that the proposed method's fault classification capability is superior to existing methods.

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Slicing Resource Allocation Based on Dueling DQN for eMBB and URLLC Hybrid Services in Heterogeneous Integrated Networks

Cited by 6 publications

References 36 publications

Retrial Queueing System for Analyzing Impact of Priority Ultra-Reliable Low-Latency Communication Transmission on Enhanced Mobile Broadband Quality of Service Degradation in 5G Networks

Retrial Queueing System for Analyzing Impact of Priority Ultra-Reliable Low-Latency Communication Transmission on Enhanced Mobile Broadband Quality of Service Degradation in 5G Networks

A Hierarchical Resource Scheduling Method for Satellite Control System Based on Deep Reinforcement Learning

A reinforcement neural architecture search convolutional neural network for rolling bearing fault diagnosis

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